Date of Award

6-9-2006

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Yanqing Zhang - Chair

Second Advisor

Yi Pan

Third Advisor

Rajeshekhar Sunderraman

Abstract

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy.

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